Comparative Analysis of Morphological and Reproductive Traits in Queens of Different Genotypes Reared in a Temperate Climate
International Conference on Mathematics and Applied Data Science (ICMADS’25), Konya, Türkiye, 29 - 31 Ağustos 2025, ss.221-222, (Özet Bildiri)
- Yayın Türü: Bildiri / Özet Bildiri
- Basıldığı Şehir: Konya
- Basıldığı Ülke: Türkiye
- Sayfa Sayıları: ss.221-222
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Atatürk Üniversitesi Adresli: Evet
Özet
The study conducted at the Atatürk University Food and Livestock Research and Application Center delved into the intricacies of queen bee quality assessment, focusing on both morphological and reproductive traits. Analysis of reproductive characteristics revealed significant differences among queen genotypes, particularly in pre-oviposition periods, emergence weight, spermatheca diameter, and spermatozoa count. Cold-climate queens, such as the Caucasian, Carniolan, and Carpathian genotypes, exhibited delayed egg-laying compared to other genotypes. Queens reared from 1-day-old larvae exhibited an average pre-oviposition period of 13.29±0.21days. Significant differences (P<0.05) were observed among genotypes regarding the pre-oviposition period. Emergence weight, a key indicator of queen quality, varied across genotypes. There was no difference between genotypes in terms of emergence weight, but larval age affected emergence weight. While the highest diameter of spermathecae value was obtained from the Italian genotype, the difference between the genotypes was found to be insignificant. The genotypes were divided into two groups in terms of spermatozoa number, and it was observed that the Carpathian and Carniolan genotypes were in the first place. The difference between genotypes was found to be significant (P<0.05). It was determined that the Caucasian genotype stood out in terms of head length, while the Carpathian genotype had the highest values in terms of head width. In this study, a significant amount of data was gathered by analyzing multiple parameters. To facilitate a clearer understanding of both the associations among the data and their distribution across analyzed parameters, principal component analysis (PCA) was applied. PCA has been highlighted as a valuable method for simplifying complex multivariate datasets by reducing them to a smaller set of principal components.